1 Loss Triangle Philosophy Leigh J. Halliwell, FCAS, MAAA Consulting Actuary CARe Seminar Boston, MA May 19, 2008 Leigh J. Halliwell, FCAS, MAAA Consulting Actuary CARe Seminar Boston, MA May 19, 2008

2 Models Versus Methods “We will debate whether we should be giving guidance to actuaries about picking development factors, or whether we should be counseling them to drop chain-ladder methods in favor of some other technique.” (from the session description) “Inevitable failures [of actuarial judgment] will only … discredit the profession. Actuaries must not presume to judge what they cannot scientifically model.” (Halliwell, 2007, footnote 5) One reasonable model with good diagnostics is better than (i.e., more informative than) any number of methods. “We will debate whether we should be giving guidance to actuaries about picking development factors, or whether we should be counseling them to drop chain-ladder methods in favor of some other technique.” (from the session description) “Inevitable failures [of actuarial judgment] will only … discredit the profession. Actuaries must not presume to judge what they cannot scientifically model.” (Halliwell, 2007, footnote 5) One reasonable model with good diagnostics is better than (i.e., more informative than) any number of methods.

3 Linear (Regression) Models “Regression toward the mean” coined by Sir Francis Galton ( ). The real problem: Finding the Best Linear Unbiased Estimator (BLUE) of vector y 2, vector y 1 observed. y = X  + e. X is the design (regressor) matrix.  unknown; e unobserved, but (the shape of) its variance is known. For the proof of what follows see Halliwell [1997] “Regression toward the mean” coined by Sir Francis Galton ( ). The real problem: Finding the Best Linear Unbiased Estimator (BLUE) of vector y 2, vector y 1 observed. y = X  + e. X is the design (regressor) matrix.  unknown; e unobserved, but (the shape of) its variance is known. For the proof of what follows see Halliwell [1997]

4 The Formulation

5 The BLUE Solution

6 Remarks on the Linear Model Actuaries need to learn the matrix algebra. Excel OK; but statistical software is desirable. X 1 of is full column rank,  11 non-singular. Linearity Theorem: Model is versatile. My papers (cf. References) describe complicated versions. Actuaries need to learn the matrix algebra. Excel OK; but statistical software is desirable. X 1 of is full column rank,  11 non-singular. Linearity Theorem: Model is versatile. My papers (cf. References) describe complicated versions.

7 Regression toward the Mean To intercept or not to intercept?

8 What to do? Ignore it. Add an intercept. Barnett and Zehnwirth [1998] 10-13, notice that the significance of the slope suffers. The lagged loss may not be a good predictor. Intercept should be proportional to exposure. Explain the torsion. Leads to a better model? Ignore it. Add an intercept. Barnett and Zehnwirth [1998] 10-13, notice that the significance of the slope suffers. The lagged loss may not be a good predictor. Intercept should be proportional to exposure. Explain the torsion. Leads to a better model?

9 Galton’s Explanation Children's heights regress toward the mean. Tall fathers tend to have sons shorter than themselves. Short fathers tend to have sons taller than themselves. Height = “genetic height” + environmental error A son inherits his father’s genetic height:  Son’s height = father’s genetic height + error. A father’s height proxies for his genetic height. A tall father probably is less tall genetically. A short father probably is less short genetically. Excellent discussion in Bulmer [1979] Cf. also sportsci.org/resource/stats under “Regression to Mean.” Children's heights regress toward the mean. Tall fathers tend to have sons shorter than themselves. Short fathers tend to have sons taller than themselves. Height = “genetic height” + environmental error A son inherits his father’s genetic height:  Son’s height = father’s genetic height + error. A father’s height proxies for his genetic height. A tall father probably is less tall genetically. A short father probably is less short genetically. Excellent discussion in Bulmer [1979] Cf. also sportsci.org/resource/stats under “Regression to Mean.”

10 The Lesson for Actuaries Loss is a function of exposure. Losses in the design matrix, i.e., stochastic regressors (SR), are probably just proxies for exposures. Zero loss proxies zero exposure. The more a loss varies, the poorer it proxies. The torsion of the regression line is the clue. Reserving actuaries tend to ignore exposures – some even glad not to have to “bother” with them! SR may not even be significant. Loss is a function of exposure. Losses in the design matrix, i.e., stochastic regressors (SR), are probably just proxies for exposures. Zero loss proxies zero exposure. The more a loss varies, the poorer it proxies. The torsion of the regression line is the clue. Reserving actuaries tend to ignore exposures – some even glad not to have to “bother” with them! SR may not even be significant.

11 Example in Excel

12 References Barnett, Glen, and Ben Zehnwirth, “Best Estimates for Reserves,” PCAS LXXXVII (2000), Bulmer, M.G., Principles of Statistics, Dover, Halliwell, Leigh J., “Loss Prediction by Generalized Least Squares, PCAS LXXXIII (1996), “, “Statistical and Financial Aspects of Self-Insurance Funding,” Alternative Markets / Self Insurance, 1996, “, “Conjoint Prediction of Paid and Incurred Losses,” Summer 1997 Forum, “, “Chain-Ladder Bias: Its Reason and Meaning,” Variance, Fall 2007, Judge, George G., et al., Introduction to the Theory and Practice of Econometrics, Second Edition, Wiley, Barnett, Glen, and Ben Zehnwirth, “Best Estimates for Reserves,” PCAS LXXXVII (2000), Bulmer, M.G., Principles of Statistics, Dover, Halliwell, Leigh J., “Loss Prediction by Generalized Least Squares, PCAS LXXXIII (1996), “, “Statistical and Financial Aspects of Self-Insurance Funding,” Alternative Markets / Self Insurance, 1996, “, “Conjoint Prediction of Paid and Incurred Losses,” Summer 1997 Forum, “, “Chain-Ladder Bias: Its Reason and Meaning,” Variance, Fall 2007, Judge, George G., et al., Introduction to the Theory and Practice of Econometrics, Second Edition, Wiley, 1988.